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López-Bautista AO, Flores-Tlacuahuac A, Nigam K. Optimal Start-up Policies for a Nanofluid-Based Solar Thermal Power Plant. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b03217] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
| | | | - K.D.P. Nigam
- School of Engineering and Science, Tecnologico de Monterrey, Monterrey, Nuevo Leon 64849, Mexico
- Department of Chemical Engineering, Indian Institute of Technology, Delhi 600036, India
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Integration of optimal cleaning scheduling and control of heat exchanger networks under fouling: MPCC solution. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2019.04.012] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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3
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Hansen B, Tolbert B, Vernon C, Hedengren JD. Model predictive automatic control of sucker rod pump system with simulation case study. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2018.08.018] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Abstract
This paper introduces GEKKO as an optimization suite for Python. GEKKO specializes in dynamic optimization problems for mixed-integer, nonlinear, and differential algebraic equations (DAE) problems. By blending the approaches of typical algebraic modeling languages (AML) and optimal control packages, GEKKO greatly facilitates the development and application of tools such as nonlinear model predicative control (NMPC), real-time optimization (RTO), moving horizon estimation (MHE), and dynamic simulation. GEKKO is an object-oriented Python library that offers model construction, analysis tools, and visualization of simulation and optimization. In a single package, GEKKO provides model reduction, an object-oriented library for data reconciliation/model predictive control, and integrated problem construction/solution/visualization. This paper introduces the GEKKO Optimization Suite, presents GEKKO’s approach and unique place among AMLs and optimal control packages, and cites several examples of problems that are enabled by the GEKKO library.
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Beal LD, Park J, Petersen D, Warnick S, Hedengren JD. Combined model predictive control and scheduling with dominant time constant compensation. Comput Chem Eng 2017. [DOI: 10.1016/j.compchemeng.2017.04.024] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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6
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Review of Field Development Optimization of Waterflooding, EOR, and Well Placement Focusing on History Matching and Optimization Algorithms. Processes (Basel) 2017. [DOI: 10.3390/pr5030034] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This paper presents a review of history matching and oil field development optimization techniques with a focus on optimization algorithms. History matching algorithms are reviewed as a precursor to production optimization algorithms. Techniques for history matching and production optimization are reviewed including global and local methods. Well placement, well control, and combined well placement-control optimization using both secondary and tertiary oil production techniques are considered. Secondary and tertiary recovery techniques are commonly referred to as waterflooding and enhanced oil recovery (EOR), respectively. Benchmark models for comparison of methods are summarized while other applications of methods are discussed throughout. No single optimization method is found to be universally superior. Key areas of future work are combining optimization methods and integrating multiple optimization processes. Current challenges and future research opportunities for improved model validation and large scale optimization algorithms are also discussed.
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